Reaction-diffusion systems

Consider a reaction-diffusion system with two components \(u(x, t)\) and \(v(x, t)\): \[ \begin{align} \frac{\partial u(x,t)}{\partial t} & = \gamma \cdot f(u,v) + \frac{\partial^2 u(x,t)}{\partial x^2}, \\ \frac{\partial v(x,t)}{\partial t} & = \gamma \cdot g(u,v) + d \cdot \frac{\partial^2 v(x,t)}{\partial x^2} \end{align} \] At the boundary, \(u\) and \(v\) satisfy the zero-flux condition. We will simulate the system numerically in discrete space. Based on \(N\) equidistant supporting points, the distance between neighboring points is \(\Delta x = \frac{1}{N-1}\). The resulting approximation of the diffusion term reads

\[ \begin{align} \frac{\partial^2 u(x,t)}{\partial x^2} \quad \longrightarrow \quad \frac{u(x - \Delta x) - 2 u(x) + u(x + \Delta x)}{(\Delta x)^2}, \end{align} \] and analogously for \(v\). Thereby, the partial differential equation turns into a system of ordinary differential equations.

Exercise 1: Turing pattern formation

Implement the linear Turing system with the reaction functions

\[ \begin{align} f(u,v) & = a \cdot u + b \cdot v, \\ g(u,v) & = c \cdot u + e \cdot v, \end{align} \] with parameters \(a = -2\), \(b = 2.5\), \(c = -1.25\), \(\gamma = 1000\), \(d = 0.5\), \(e = 1.5\), and \(N = 250\). Let the initial values \(u_{1...N} \propto N(0,1)\) and \(v_{1...N} \propto N(0,1)\) be normally distributed random numbers.

Exercise 2: Activator-inhibitor model of Gierer and Meinhardt

Implement the (dimensionless) activator-inhibitor model with the reaction functions

\[ \begin{align} f(u,v) & = a - b\cdot u + \frac{u^2}{v}, \\ g(u,v) & = u^2 - v, \end{align} \]

and parameters \(a = 0.1\), \(b = 1\), \(\gamma = 100\), \(d = 10\), and \(N = 250\). Use the homogeneous equilibrium states, \[ \begin{align} u_{1...N} = \frac{a + 1}{b} \quad \text{and} \quad v_{1...N} = \left( \frac{a + 1}{b} \right)^2, \end{align} \] and add noise to obtain initial values.

Cathedral exercise

What distinguished our cathedral from all other German gothic cathedrals?